1. 1
Evaluating the impact of climate change
on sea level rise in the Chesapeake Bay
29th April 2016
12-718: Environmental Engineering, Sustainability, and Science Project
By:
Sriram Bharadwaj,
Hongtian Carlos Lin Chen,
Xiruo Wang,
Dawning Wu,
Sriniwasa Prabhu

2. 2
Abstract
Global climate change has existed throughout the history of the world. However, the
warming trend in the past 1300 years has been proceeding at a rate that is unprecedented,
very likely due to human activities. Global sea level rise is a significant indicator for this
burgeoning climate change. According to an article from ScienceNews this year, the rate
of sea level rise is about 35 centimeters (0.35 meter), which is twice the rate as it was in
the previous century. This paper provides elaborate and detailed analysis and prediction
of sea level rise over ten observation stations in Chesapeake Bay, focusing on two
elements; the sea level rise from the mean sea level datum of each station and the storm
surges found from extracting the decadal signal from the reported sea level trend
numbers. This paper reports the results from conducting linear regression analysis for sea
level trend, probabilistic assessment of multiple extreme value distributions of the storm
surge values, and subsequent ArcGIS mapping of the results for the areas of the three
worst-case scenario stations: Washington, D.C.; Solomons Island, MD; and Gloucester
Point, VA. Finally, the results are assessed for economic and industrial impact for the
three worst-case scenario stations. The final mean sea level predicted for each of the
worst-case scenarios were the following, respectively: 2.485 m, 2.070 m, and 1.665 m.
The most negatively impacted city by sea level rise was determined to be Washington
D.C., not only due to the worst sea level rise but also based on the current economic
situation. Solomons Island, MD and Gloucester Point, VA would be impacted less
because they have relatively lower population numbers, as well as lower economic
growth rates. Since such is the case, although sea level rise provides an excellent way to
start the impact analysis, this paper is limited in its scope and analysis methodologies;
there are more influential factors that should be counted in the study for more detailed
analysis.

3. 3
Table of Contents
1. Introduction ........................................................................................................................... 4
2. Sea level dynamic................................................................................................................... 5
3. Methodology for sea level rise prediction and economic impact analysis ........................ 6
3.1 Sea level rise.......................................................................................................................... 6
3.2 Storm surge............................................................................................................................ 7
4. Sea level rise and inundation mapping results.................................................................... 9
5. Economic impacts................................................................................................................ 18
5.1 Economy of Washington D.C.............................................................................................. 19
5.2 Economy of Solomon Islands, Maryland ............................................................................ 19
5.3 Economy of Gloucester Point, Virginia .............................................................................. 19
5.4 Overall Impact in Chesapeake bay...................................................................................... 19
5.4.1 Impact on Infrastructure ............................................................................................... 20
5.4.2 Impact due to Change in Shoreline............................................................................... 20
5.4.3 Socio-economic Impact................................................................................................ 20
6. Discussion............................................................................................................................. 21
6.1 Sea Level Rise Prediction.................................................................................................... 21
6.2 Merit of the study ................................................................................................................ 21
7. Project/Model Limitations.................................................................................................. 22
8. Conclusion............................................................................................................................ 24

4. 4
1. Introduction
Chesapeake Bay is the largest estuary in the North America; it stretches about 320 km
from the northern bay in Maryland to the southern bay in Virginia. The bay is calculated
to have a surface area of approximately 4,470 square miles (11,601 km2
).1
Due to its
large coverage of area, Chesapeake Bay naturally has greater potential to yield and
facilitate an extraordinary economic system. According to the Water Resources Agency
at the University of Delaware, Chesapeake Bay brings in more than $2 billion of revenue
annually from industry, ecotourism, agriculture and other economic sectors2
.
Figure 1: Chesapeake Bay Image from Google Map
In light of the significant potential productivity made possible by the bay, it is also
situated and thus described as a highly vulnerable natural resource. Due to the low-lying
nature of the land in between the water and the land, Chesapeake Bay is susceptible to the
inundation risks posed by rises in tidal waves (wave action) and storm surges, which can
easily cause great damage to the local infrastructures and economy with one disastrous
event 3
.

5. 5
The Intergovernmental Panel on Climate Change (IPCC) has delineated multiple
scenarios related to climate change, most notably RCP 8.5 (continued emissions - status
quo). RCP 8.5 predicts that by the end of this century, in 2100, the global mean sea level
will rise between 52 to 98 cm4
. However, according to the National Wildlife Federation,
the Chesapeake Bay sea level will rise up to 78.7 inches, almost 2 meters and twice as
high as the global mean sea level rise value5
. Meanwhile, different stations in the
Chesapeake Bay has record different sea level data, which means different places will
suffer various inundation. Therefore, further research needs to be done to project sea
level rise in different stations by the end of this century.
The purpose of this project is to find the worst-case scenario of sea level rise from now to
one hundred years later, that is to say, in the year of 2115, which place will be most
affected and what is the predicted sea level at that time. Therefore, in this report, we have
chosen 10 National Water Level Observation Network (NWLON) stations in the
Chesapeake Bay, use mathematical analysis to address the record sea level data and
predict the future sea level of the 10 stations considering the extreme water level like
storm surges. After projecting the future sea level value, a GIS map is created base on the
sea level data and the inundation zone is shown in the GIS map. With the GIS map, we
can easily find which places have the greatest effect of sea level rise and what the
economy impact are.
2. Sea level dynamic
Sea level is a complex subject with many layers of interpretation. Therefore, it is essential
for all terminology used to be initially defined and introduced, according to the order of
appearance in the report, before describing the project itself.
Tidal datum
Tidal datum refers to the vertical reference for water levels in tidal waterways. In the
United States, the National Ocean Service within the National Oceanic and Atmospheric
Administration (NOAA) has adopted the 19-year period, also known as the National
Tidal Datum Epoch (NTDE), as the official time segment. Over this time segment, it
provides the tidal datums by obtaining mean values, and the present epoch is from 1983
through 2001. For example, the tidal datums include mean sea level (MSL), which is the
average of recorded hourly heights, mean higher high water (MHHW), which is the
average of the higher high waters recorded during each tidal day.

6. 6
Figure 1.1 Representation of Tidal Datums
Source: tidesandcurrents.noaa.gov
Relative sea level
Sea level can be measured in several ways. Relative sea level is the sea level that results
from considering impact on sea level from both the rise in water level and any changes in
the land level due to subsidence. Relative sea level values are obtained from NOAA.
3. Methodology for sea level rise prediction and economic impact analysis
There are two components to our analysis: sea level trend and storm surge prediction.
The sea level trend involves a stochastic dataset, which requires data analysis to consider
how the data will change over time. That is, future sea level trends cannot be entirely
based on present trends. There must be an element of data extrapolation, by which the sea
level trend data is projected into the future.
There are several method to predict this sea level trend, in this project, the linear
regression function will be used to predict different station’s sea level rise, meanwhile,
different probability distributions are used to calculate the 100 year return period of the
storm surge. Finally, we will join the sea level and the 100 year return period of the storm
surge together to predict the total sea level rise in 2115.
3.1 Sea level rise
To estimate the sea level rise trend of the different region in Chesapeake Bay, two simple
models are used here: linear regression model and curvilinear regression model. The
linear regression model is very commonly used in such analyses 6 7
to protect future sea
level trends in a smaller (less than 100 years) time period.
Before using the linear regression model to deal with the record sea level data, some data
processing have been done. The seasonal cycle data is removed from the raw data, since
it violates the assumption that the residual errors are random normally distributed. In

7. 7
addition, after removing the seasonal cycle, it can reduce the mean square deviation and
the standard error of the regression. For the decadal variability, the low-pass filter method
is used here to cutoff a period of 2 years, that is to say, for 24 months and filter width of
24 months, producing a motion that presents a period of 100 months and longer.
Linear regression model use the predictor value x as time in year and the expected value
y as sea level rise in millimeter. The regression equation yields the trend is shown below:
𝑦𝑦 = 𝑏𝑏1 𝑥𝑥 + 𝑏𝑏0 + ε
where y is the sea level rise value, x is the time value, b1 and b0 are regression
coefficients, which represent the slope and the y-intercept, respectively, and where ε
value is the residual term with zero mean and variance.
Curvilinear regression model is the integrated equation of linear regression, it is used to
evaluate the acceleration or deceleration of the sea level trend, in other words, the time-
variable rate of sea level rise or fall. The Curvilinear regression equation is shown below:
𝑦𝑦 = 𝑏𝑏2 𝑥𝑥2
+ 𝑏𝑏1 𝑥𝑥 + 𝑏𝑏0 + ε
where y is the sea level rise value, x is the time value, b2 is the quadratic coefficient, b1
and b0 are different parameters for the equation, and where ε value is the residual term
with zero mean and variance.
However, since using the mean sea level data, the curvilinear regression model will have
a parabola behavior, which means when it gets to the peak of the figure, the regression
decline, and as time goes by, the mean sea level will decrease. This result is no stable and
makes no sense, therefore, in this project, we will not use the curvilinear regression and
only consider on the linear regression.
3.2 Storm surge
The analytical process is different for the second component of storm surge prediction.
Storm surge is a manifestation of extreme climate. Due to the unpredictable nature of
climate, it is therefore assessed as a random dataset. In other words, the range of storm
surge levels will not change significantly between now and 2100 6
. Since such is the case,
storm surge data can be fitted to multiple probability distributions in order to predict the
100-year storm surge, assumed to be the worst-case scenario storm surge level.
Sea level trends data from 1930-2015 is provided by NOAA CO-OPS. This data from the
website provides data that has already been processed for removal of variability in the
data due to seasonal cycles.
According to the Hydrodynamic Analysis and Design Conditions EM 1110-2-1100, Log-
Pearson type 3 (LP3) distribution method is used in U.S Water Resource Council to
analyze the extreme water level value. LP3 distribution is a three parameter distribution,
with location parameter (μ), describing where on the horizontal axis the graph is located;

8. 8
scale parameter (σ), describing how the graph is spread out, the larger the scale
parameter, the more spread out the distribution; shape parameter (γ), describing how the
data is distributed without affecting the location or scale of the distribution, it is
calculated from the skewness. The Log-Pearson type 3 mathematical expression is shown
below:
𝑓𝑓(𝑥𝑥) =
1
𝐴𝐴𝐴𝐴Γ(𝑘𝑘)
�
ln(𝑥𝑥) − 𝐵𝐵
𝐴𝐴
�
𝑘𝑘−1
𝑒𝑒
−�
ln(𝑥𝑥)−𝐵𝐵
𝐴𝐴
�
Where:
µ = ln(𝑥𝑥) = 𝐵𝐵 + 𝐴𝐴𝑘𝑘
𝜎𝜎 = 𝐴𝐴√𝑘𝑘
𝑘𝑘 =
4
γ2
(skew of ln(x) corrected for bias)
The Generalized Extreme Value (GEV) distribution model has been proven to work very
well for estimating on 100 year extreme water level by the analysis and estimation of
close 100 years annual maximum water level 8
. The GEV distribution also has three
parameters, the location parameter (ξ), the scale parameter (α) and the shape parameter
(κ). The Generalized Extreme Value distribution mathematical expression is shown
below:
𝑓𝑓(𝑥𝑥) =
𝑒𝑒−(1−𝑘𝑘)𝑦𝑦−𝑒𝑒−𝑦𝑦
𝛼𝛼
Where:
𝑦𝑦 = −
log �1 −
𝐾𝐾(𝑥𝑥 − 𝜉𝜉)
α
�
𝐾𝐾
, 𝑤𝑤ℎ𝑒𝑒𝑒𝑒 𝑘𝑘 ≠ 0
The Weibull Distribution model is another distribution model that can be applied for
extreme value analysis of high storm water event. The Weibull Distribution has two
parameters, the shape parameter (κ) and the scale parameter (λ). The Weibull Distribution
mathematical expression is shown below:
𝑓𝑓(𝑥𝑥) =
𝐾𝐾
λ
�
𝑥𝑥
λ
�
𝑘𝑘−1
𝑒𝑒
−�
𝑥𝑥
λ
�
𝐾𝐾
The GEV, Weibull and LP3 distributions used in this report, all have a wide variety of
applications for estimating extreme values of storm surges and are all widely used.
Therefore, in this project, we will not choose the best distribution by the R square value,
instead, we will find the mean of these three distributions and use this value to calculate
the 100 return period of the storm surge.

9. 9
4. Sea level rise and inundation mapping results
Linear Regression analysis was performed for eight stations which are listed in
figures 4.1 - 4.8. Lewisetta and Chesapeake bay bridge tunnel stations have large
data gaps. Linear regression for these three stations resulted in unstable results.
Hence they were not included in the study. As stated before, three worst stations
according to the regression analysis was studied in detail to include storm surge
in the station area.
Figure 4.1 Annapolis Station
Figure 4.2 Baltimore Station

10. 10
Figure 4.3 Cambridge Station
Figure 4.4 Gloucester Point Station

11. 11
Figure 4.5 Kiptopeke Station
Figure 4.6 Solomons Islands Station

12. 12
Figure 4.7 Sewells Point Station
Figure 4.8 Washington Station

13. 13
As described in methodology, a 100 year time period was computed for storm surge data
in the three worst stations namely Washington, D.C.; Solomons Islands; and Gloucester
Point. The plots of the fits of storm surge data from Gloucester point station with Log
Pearson III, Generalized Extreme Value and Weibull distributions in Figure 4.9-4.11. The
mean cumulative probability density (CDF) was computed as mean by assigning weights
to each of these stations based on goodness of fit test (Chi-square test statistic). The 100
year time period was computed from the mean CDF for all the three stations.
Figure 4.9 Log Pearson type III distribution plot for Gloucester Point station
Figure 4.10 Weibull distribution fit for Gloucester Point station

14. 14
Figure 4.11 Generalized Extreme Value distribution fit for Gloucester Point station
Finally the inundation maps were created in GIS from the combined values of sea level
rise and storm surge computed. The combined results are tabulated in Table 4.1. The
inundation simulations are shown in figure 4.12-4.14.
Table 4.1 Combined results of Sea Level Rise and Storm Surge
Stations Sea Level
Rise(m)
Storm Surge (m) Final sea level in 2115 (m)
Solomons Island, MD 0.499 1.571 2.070
Gloucester Point, VA 0.561 1.104 1.665
Washington, DC 0.417 2.069 2.486

15. 15
Figure 4.12 GIS inundation map for Solomon Islands

16. 16
Figure 4.13 GIS inundation map for Gloucester Point

17. 17
Figure 4.14 GIS inundation map for Washington Station

18. 18
5. Economic impacts
Low-lying coastal areas infrastructure and their stock is at an increasing risk to damage
from sea-level rise inundation, extreme astronomical tides, storm surge flooding,
hurricanes, and other storm events 9
. This risk continues to increase due to the continuing
growth of coastal cities and tourism 10
. Damage cost estimations due to increasing sea level
are often substantial 11
. Rising sea level contributes to the redistribution of sediment along
sandy coasts. In the long-term it can also lead to landward migration of barrier islands
through offshore and onshore transport of sediment 11
. Fixed structures and changes in
vegetation, block coastal habitats landward migration creating coastal squeeze and
increasing impact from sea-level rise. Coastal squeeze is the squeeze of salt marshes,
mangroves, and flats between rising sea level and naturally or artificially fixed shoreline 9
.
When landward migration of coastal marshes is impeded by barriers and slope and the
vertical growth becomes slower than the sea level rise, submergence occurs 12
. When
marshes drown and are converted to open water, the tidal exchange at inlets increases and
leads to sand isolation in the deltas and increases erosion of adjacent shorelines 11
.
Small islands lack reliable demographic and socio-economic scenarios and projection,
which results in future changes in socio-economic conditions of small islands not being
presented well in existing assessments9
. The impacts of sea-level rise include more intense
storms, and climate change without adaptation or mitigation on the small islands, will be
substantial due to inundation, storm surge, erosion, and other coastal hazards . These
hazards are threatening the infrastructure, local resources, settlements and facilities that are
the livelihood of island communities 13
. Bates suggests that some islands and low-lying
coastal areas may become unlivable by 2100 13
.
The IPCC has a very high confidence that coastal communities and habitats will be
progressively stressed due to the climate change impacts combined with development and
pollution 14
. The rate of sea-level rise is expected to increase in the future along the coast
14
. Along the Gulf and Atlantic coasts it's predicted that storm impacts will become
increasingly severe 9
. Population growth, demand for waterfront housing property, and
rising value of the infrastructure along the coast increases this vulnerably 9
. With
Chesapeake Bay region being one of America’s most important economic regions, this part
of the report aims to look at some of the economic vulnerabilities in the regions with the
highest impact due to sea-level rise.
The Chesapeake Bay is more than an extraordinary ecological system – it’s also an
incredibly valuable industry. Like any other system, every part – rivers, wetlands, forests,
animals, people and so on – has a role in the whole “machine” working at optimum levels.
In 2004, the Blue Ribbon Finance Panel report estimated that the Bay’s value was more
than one trillion dollars related to fishing, tourism, property values and shipping activities
15
. If you were to adjust that figure for inflation, the number would have increased to
roughly $1.144 trillion by 2011 15
. A recent report released by the Chesapeake Bay
Foundation 16
provides some more specific examples of the economic benefits associated
with the Chesapeake Bay ecosystem. The commercial seafood industry in Maryland and

19. 19
Virginia contributed $2 billion in sales, $1 billion in income, and more than 41,000 jobs to
the local economy. The saltwater recreational fishery contributed $1.6 billion in sales,
which then created more than $800 million of additional economic activity and roughly
13,000 jobs. Activities related to recreational striped bass fishing (such as expenses, travel
and lodging) generates roughly $500 million of economic activity. Further upstream, nearly
two million people go fishing in Pennsylvania each year, contributing more than $1.6
billion to the economy. If you were to add income generated from forestry, recreational
boating, ecotourism, heritage tourism, shipping, and many other industries, it’s easy to see
how the “Chesapeake Bay Industry” could reach one trillion dollars.
5.1 Economy of Washington D.C.
The gross state product of the District in 2010 was $103.3 billion, which would rank it No.
34 compared to the 50 U.S. states 17
. The gross product of the Washington Metropolitan
Area was $425 billion in 2010, making it the fourth-largest metropolitan economy in the
United States 17
. In 2012, the federal government accounted for about 29% of the jobs in
Washington, D.C. 18
. Many organizations such as law firms, independent contractors (both
defense and civilian), non-profit organizations, lobbying firms, trade unions, industry trade
groups, and professional associations have their headquarters in or near D.C. to be close to
the federal government 18
. Tourism is Washington's second largest industry.
Approximately 18.9 million visitors contributed an estimated $4.8 billion to the local
economy in 2012 19
.
5.2 Economy of Solomon Islands, Maryland
Solomons, also known as Solomons Island, is an unincorporated community and census-
designated place (CDP) in Calvert County, Maryland, United States. The population was
2,368 at the 2010 census, up from 1,536 at the 2000 census 20
. Solomons is a popular
weekend destination spot in the Baltimore–Washington metropolitan area.The
unemployment rate in Solomons is 4.70 percent (U.S. avg. is 6.30%) 21
. Recent job growth
is positive. Solomons jobs have increased by 0.19 percent 21
.Compared to the rest of the
country, Solomon's cost of living is 19.40% higher than the U.S. average 21
.
5.3 Economy of Gloucester Point, Virginia
Gloucester Point is a census-designated place (CDP) in Gloucester County, Virginia,
United States 22
. The population was 9,402 at the 2010 census 22
. It is also home to The
College of William & Mary's Virginia Institute of Marine Science, a graduate school for
the study of oceanography. The unemployment rate in Gloucester Point, Virginia, is 4.60%,
with job growth of 1.72% 21
. Future job growth over the next ten years is predicted to be
38.80% 21
.
5.4 Overall Impact in Chesapeake bay
Among the three regions with highest expected sea-level rise in the Chesapeake Bay region,
Washington D.C. would have the worst economic impact. This is so because Washington
D.C. is the most populous area in the region and has the highest economic activity in the

20. 20
region. The second highest impact would be in Gloucester Point and then in Solomon
Islands. Although Gloucester Point and Solomons Islands are relatively smaller, they are
up and coming regions with a relatively high population and economic growth rate. The
change of shoreline due to sea level rise would affect the property and property rates in
each of the three regions. It would also grossly impact the tourism in the region. It is
important to note that not only will households and families be displaced by rising sea
levels but a significant amount of infrastructure, specifically buildings will be impacted as
well.
5.4.1 Impact on Infrastructure
Rise in sea level would adversely impact the infrastructure in the region. Many critical
structure like the Washington D.C.’s Ronald Reagan Washington National Airport, Gravity
Point Park, West Potomac Park, South Glebe Road, Eisenhower Avenue and other major
roads, avenues, parks, bridges etc would be drastically affected 10
. A lot of high end houses,
museums, corporate buildings would be impacted. The area around the Meadowood
Recreation Area which houses a lot of poor people would be inundated. In Gloucester Point
VA, neighborhoods in the Guinea Road region and Dutton Road region would be
completely inundated 10
. These areas house mostly middle class people and also have small
and medium sized businesses. The College of William & Mary's Virginia Institute of
Marine Science which has over 400 people working would also be affected 12
. Solomons
Island would be adversely affected. Although it is a small city with a population of less
than 2500 people, the city is expected to grow rapidly as a tourist hotspot for people in the
D.C-Baltimore area 10
. A lot of hotels, Bed & Breakfast inns, houses and small time
businesses in the city would be gravely impacted 13
. If we consider nearby regions in the
city then nuclear power plants, factories, thermal power plants etc. would be affected 14
.
This would be a catastrophic situation as something similar to the Fukushiima Daiichi
Nuclear Disaster could happen.
5.4.2 Impact due to Change in Shoreline
As the sea rises, it would impact the current shorelines and can alter the way the city would
have its shoreline in the next hundred years 18
. Areas near the shores like parks, downtown
commercial areas, prime real estate properties, bridges, beaches etc would be affected 19
.
This may cause a lot of displacement of people and can hurt the infrastructure in all the
three regions.
5.4.3 Socio-economic Impact
Sea-level rise could have a domino effect on the socio-economic situation in the region 14
.
Due to inundation of infrastructure and change in shoreline, a lot of people would be
displaced and affected. Poor people and minorities especially in Washington D.C. would
be worst hit 21
. Hospitals in the D.C. region would be affected and this in turn could affect
the medical services in all the three regions.

21. 21
5.4.4 Impact due to Hurricanes
Hurricanes would have double the impact with rise in sea levels. Anapolis witnessed a
tremendous impact due to Hurricane in 2009 with critical infrastructure, medical services,
provision of utilities being drastically affected 15
. With the number of hurricanes and their
impacts increasing with climate change, the region is not prepared to deal with major
hurricanes 15
. This would have huge economic losses in the region.
6. Discussion
6.1 Sea Level Rise Prediction
The predicted sea level rise computed in this side included both the increase of sea water
level from the mean sea level and the increase in water level due to storm surge. The
study resulted in 100 year time period for storm surges for the three worst stations 1.57m,
1.104m and 2.069m. This can be inferred as these values have 1% probability of
occurring in every year. For an even more robust analysis 0.1% probable values of storm
surge should be taken. However in practice 100 year time period is adopted in most of the
studies in the domain of risk analysis. The results from this analysis can be used for
shoreline management to safeguard infrastructure and businesses.
6.2 Merit of the study
Sea Level Rise prediction usually never consider storm surge in their analysis. It is very
important to include random and at the same time catastrophic events like storm surges in
sea level rise analysis. Storm surges have unexpected impacts on the shoreline. However
it is very hard to simulate storm surges. Hence it is modelled as a random event and the
worst case storm surge that could occur with 1% probability is included in the analysis.
This is an advanced probabilistic approach to sea level rise analysis. However to
completely evaluate the impact on the shoreline wave run up should be included in the
study. The wave action requires a physical simulation model.

22. 22
7. Project/Model Limitations
A complete model of sea level rise would include in-depth analysis of not only the
unpredictable nature of storm surge, but also of that of wind and wave action. That is, the
context of sea level rise impact is always civilization. Sea level rise and storm surge are
not the only aspects of water level that results in economic losses for civilization. Wind
and wave action are elements that were not within the scope of this report, and therefore
point to the limitations of this research paper. As can be seen in the diagram below, other
levels include Mean Higher-High Water and Mean High Water levels, which measure the
total water level values on high tide days.
Figure 6.1 NOAA Water Level Datums
In comparison with other similar studies that have been conducted, the underlying
emphasis for this paper was decreasing uncertainty in sea level rise prediction to the
greatest extent possible. The linear regression portion includes confidence intervals in
order to reduce uncertainty. However, this research did not involve determining confidence
intervals for the probability distribution portion regarding storm surge data and prediction
for the 100-year storm surge levels.
Additionally, our model takes one predicted 100-year storm surge number and adds it to
the linearly regressed value for sea level. The process used in this report considers the high
variance involved in predicting for storm surge by taking the 100-year storm surge value,
which is the storm surge level that may be expected to occur at least one in 100 years.
However, it may be yet significantly improved by applying conducting Monte Carlo
simulations of both the 100-year storm surge value and the sea level rise. That is, instead
of directly using the linearly regressed value for the 100-year sea level rise, uncertainty

23. 23
may be reduced by comparing results by varying both at the same time, producing a greater
range of possible values. This would also provide an opportunity to incorporate calculated
confidence intervals.
The uncertainty in the storm surge models may be still reduced as well, via using maximum
likelihood estimation or other methods, in order to generate a reasonable range of the
necessary parameters for each of the probability distributions used.
Furthermore, the GIS analysis is limited in its scope, since the digital elevation model
(DEM) files used are not consistent and therefore could not be used in conjunction to make
any overall assessments of Chesapeake Bay. With additional time, the research group may
have been able to acquire, whether via financial or other means, a set of DEM files that
may be more consistent. Consistent DEMs would enable the group to perform assessments
such as calculating the percentage of inundation in the area. The group could also compare
the inundation at the worst-case scenario stations at once, with inundation represented by
the rainbow spectrum, indicating the varying degrees of inundations. In addition, the
varying degrees of inundations could be further extrapolated to decrease uncertainty by
using the distance from adjacent monitoring station(s) as a kind of “weighting” factor in
determining the predicted inundation.
Similarly, in the probability distributions method, the uncertainty could be reduced by
using a weighting system. For this paper, the mean value from the Log Pearson Type III,
Weibull, and Generalized Extreme Value probability distributions was taken, then
interpolated for the 100-year (1% exceedance probability) storm surge level. Instead of
taking the mean, the group may instead weigh the distributions (with their reasonable
ranges for the parameters) appropriately, based on their goodness of fit. This may be
determined via many methods such as probability plots, quantile plots, and Chi-squared
values. In addition to these, future direction may include adding a weight based on
historical success of fitting probability distributions to existing storm surge data. However,
this aspect of the weight should be used with care, as it may introduce unwanted bias into
the data analysis process.
The nature of the data used limits the results as well, since the stations out of which were
chosen three worst-case scenarios had missing data. These gaps of data cause significant
bias and increase the uncertainty in the sea level rise prediction. For example, in the case
of Solomons Island, which this report identified as a worst-case scenario station, it may not
actually be a worst-case scenario. The sea level trends dataset for Solomons Island actually
has many gaps, and this could have contributed significantly to its being assessed as a
worst-case scenario station. This could also be true for Cambridge, which was not included
in the analysis, since it was not identified as a worst-case scenario.
If more time were allotted, the final sea level prediction assessment may be conducted for
all ten stations for also the storm surge and not just the sea level rise, which is how this
report decides on the worst-case scenario which would also provide a more holistic
assessment of sea level rise in Chesapeake Bay as a whole.
Despite the limitations listed in this section, the value of this paper is very high, due to its
manner of probabilistic assessment using multiple distributions, in order to provide a
conservative, worst-case scenario estimate. Given resource and time constraints, this paper

24. 24
was limited in its consideration of certain subjects, but still succeeded in fulfilling the
original aim of the research, which is to investigate how sea level rise in 100 years will
impact Chesapeake Bay.
8. Conclusion
Although global climate change has existed throughout history, the warming trend in the
past 1300 years has been proceeding at a rate that is unprecedented, very likely due to
human activities. Global sea level rise is a significant evidence for burgeoning climate
change.
This study provides elaborate and detailed analysis and prediction of sea level rise over the
ten observation stations in Chesapeake Bay, which includes linear regression analysis for
sea level trend, probabilistic assessment of multiple extreme value distributions, ArcGIS
mapping and economic and industrial analysis for three worse cities. The following graph
provides corresponding results for projection of mean sea level and extreme storm surge
with 100 years return periods.
Table 8.1 Projection of mean sea level and extreme storm surge with 100 years return periods for 10 cities
City Latitude Long Datum
Level
Mean Sea
Level in 100
Years (m)
Storm
Surge (m)
Final MSL
(m)
Washington,
D.C.
38° 52.4' N 77° 1.3'
W
1.859 0.416 2.069 2.485
Solomons
Island
38° 19' N 76° 27.1'
W
1.361 0.499 1.571 2.070
Gloucester
Point
37° 14.8' N 76° 30'
W
0.894 0.561 1.104 1.665
From the results of final mean sea level, which is the total prediction in 100 years based on
the sea level rise and 100-year value of storm surge, the report offers close-range ArcGIS
maps, which aid in visualization of economic and industrial analysis for the three worst
cities, which are Washington, D.C.; Solomons Island, MD; and Gloucester Point, VA; with
final mean sea level being 2.485m, 2.070m, 1.665m, respectively. Since we could do the
economic and industrial analysis for different cities with the similar method we mentioned
in economic part, in this report we only do the analysis for the three worst cities as examples.
Although there are some unavoidable limitations in the study due to the limited time for
the comprehensive research, the study do probabilistic assessment using multiple extreme
value distributions for a conservative, worst-case scenario estimate.
The most negatively impacted city by sea level rise is Washington D.C., not only due to
the worst sea level rise but also based on the current economic situation. Like the report
shows above, Washington D.C. takes an essential position in the U.S economic
development and have large population and employment. While Solomons Island, MD and
Gloucester Point, VA have relatively less population and less impact on the U.S economic

25. 25
development. Sum up the analysis, the report points out that there are more influenced
factors should be counted in for more detailed analysis, though sea level rise do provide an
excellent way to start the impact analysis.

26. 26
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